1. Supporting Decision Making Chapter 10 McGraw-Hill/IrwinCopyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved.

2. 10-2 Identify the changes taking place in the form and use of decision support in business Identify the role and reporting alternatives of management information systems Describe how online analytical processing can meet key information needs of managers Explain the decision support system concept and how it differs from traditional management information systems Learning Objectives

3. 10-3 Explain how the following information systems can support the information needs of executives, managers, and business professionals –Executive information systems –Enterprise information portals –Knowledge management systems Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business Learning Objectives

4. 10-4 Give examples of several ways expert systems can be used in business decision- making situations Learning Objectives

5. 10-5 Levels of Managerial Decision Making

6. 10-6 Information Quality Information products made more valuable by their attributes, characteristics, or qualities – Information that is outdated, inaccurate, or hard to understand has much less value Information has three dimensions – Time – Content – Form

7. 10-7 Attributes of Information Quality

8. 10-8 Decision Structure Structured (operational) –Procedures can be specified in advance Unstructured (strategic) –Not possible to specify procedures in advance Semi-structured (tactical) –Decision procedures can be pre-specified, but not enough to lead to the correct decision

9. 10-9 Decision Support Trends The emerging class of applications focuses on – Personalized decision support – Modeling – Information retrieval – Data warehousing – What-if scenarios – Reporting

10. 10-10 Business Intelligence Applications

11. 10-11 Decision Support Systems Decision support systems use the following to support the making of semi-structured business decisions – Analytical models – Specialized databases – A decision-maker’s own insights and judgments – An interactive, computer-based modeling process DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers

12. 10-12 DSS Model Base Model Base – A software component that consists of models used in computational and analytical routines that mathematically express relations among variables Spreadsheet Examples – Linear programming – Multiple regression forecasting – Capital budgeting present value

13. 10-13 Applications of Statistics and Modeling – Supply Chain: simulate and optimize supply chain flows, reduce inventory, reduce stock-outs – Pricing: identify the price that maximizes yield or profit – Product and Service Quality: detect quality problems early in order to minimize them – Research and Development: improve quality, efficacy, and safety of products and services

14. 10-14 Management Information Systems The original type of information system that supported managerial decision making – Produces information products that support many day-to-day decision-making needs – Produces reports, display, and responses – Satisfies needs of operational and tactical decision makers who face structured decisions

15. 10-15 Management Reporting Alternatives Periodic Scheduled Reports – Prespecified format on a regular basis Exception Reports – Reports about exceptional conditions – May be produced regularly or when an exception occurs Demand Reports and Responses – Information is available on demand Push Reporting – Information is pushed to a networked computer

16. 10-16 Online Analytical Processing OLAP – Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives – Done interactively, in real time, with rapid response to queries

17. 10-17 0nline Analytical Operations Consolidation – Aggregation of data – Example: data about sales offices rolled up to the district level Drill-Down – Display underlying detail data – Example: sales figures by individual product Slicing and Dicing – Viewing database from different viewpoints – Often performed along a time axis

18. 10-18 OLAP Configuration

19. 10-19 Geographic Information Systems GIS – DSS uses geographic databases to construct and display maps and other graphic displays – Supports decisions affecting the geographic distribution of people and other resources – Often used with Global Positioning Systems (GPS) devices

20. 10-20 Data Visualization Systems DVS – Represents complex data using interactive, three-dimensional graphical forms (charts, graphs, maps) – Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form

21. 10-21 DVS Example

22. 10-22 Using Decision Support Systems Using a decision support system involves an interactive analytical modeling process – Decision makers are not demanding pre-specified information – They are exploring possible alternatives What-If Analysis – Observing how changes to selected variables affect other variable

23. 10-23 Using Decision Support Systems Sensitivity Analysis – Observing how repeated changes to a single variable affect other variables Goal-seeking Analysis – Making repeated changes to selected variables until a chosen variable reaches a target value Optimization Analysis – Finding an optimum value for selected variables, given certain constraints

24. 10-24 Data Mining Provides decision support through knowledge discovery –Analyzes vast stores of historical business data –Looks for patterns, trends, and correlations –Goal is to improve business performance Types of analysis –Regression –Decision tree –Neural network –Cluster detection –Market basket analysis

25. 10-25 Analysis of Customer Demographics

26. 10-26 Market Basket Analysis One of the most common uses for data mining – Determines what products customers purchase together with other products Results affect how companies – Market products – Place merchandise in the store – Lay out catalogs and order forms – Determine what new products to offer – Customize solicitation phone calls

27. 10-27 Executive Information Systems EIS – Combines many features of MIS and DSS – Provide top executives with immediate and easy access to information – Identify factors that are critical to accomplishing strategic objectives (critical success factors) – So popular that it has been expanded to managers, analysis, and other knowledge workers

28. 10-28 Executive Information Systems (EIS) Combines many features of MIS and DSS Provides immediate and easy information Identifies critical success factors Features –Customizable graphical user interfaces –Exception reports –Trend analysis –Drill down capability

29. 10-29 Features of an EIS Information presented in forms tailored to the preferences of the executives using the system – Customizable graphical user interfaces – Exception reports – Trend analysis – Drill down capability

30. 10-30 Enterprise Information Portals An EIP is a Web-based interface and integration of MIS, DSS, EIS, and other technologies – Available to all intranet users and select extranet users – Provides access to a variety of internal and external business applications and services – Typically tailored or personalized to the user or groups of users – Often has a digital dashboard – Also called enterprise knowledge portals

31. 10-31 Benefits of Expert Systems Captures human experience in a computer-based information system Limitations of Expert Systems Limited focus Inability to learn Maintenance problems Development cost Can only solve specific types of problems in a limited domain of knowledge

32. 10-32 Dashboard Example

33. 10-33 Enterprise Information Portal Components

34. 10-34 Enterprise Knowledge Portal

35. 10-35 Artificial Intelligence (AI) AI is a field of science and technology based on – Computer science – Biology – Psychology – Linguistics – Mathematics – Engineering The goal is to develop computers than can simulate the ability to think – And see, hear, walk, talk, and feel as well

36. 10-36 Attributes of Intelligent Behavior Some of the attributes of intelligent behavior – Think and reason – Use reason to solve problems – Learn or understand from experience – Acquire and apply knowledge – Exhibit creativity and imagination – Deal with complex or perplexing situations

37. 10-37 Attributes of Intelligent Behavior Attributes of intelligent behavior (continued) – Respond quickly and successfully to new situations – Recognize the relative importance of elements in a situation – Handle ambiguous, incomplete, or erroneous information

38. 10-38 Domains of Artificial Intelligence

39. 10-39 Cognitive Science Applications in the cognitive science of AI – Expert systems – Knowledge-based systems – Adaptive learning systems – Fuzzy logic systems – Neural networks – Genetic algorithm software – Intelligent agents Focuses on how the human brain works and how humans think and learn

40. 10-40 Robotics AI, engineering, and physiology are the basic disciplines of robotics – Produces robot machines with computer intelligence and humanlike physical capabilities This area include applications designed to give robots the powers of – Sight or visual perception – Touch – Dexterity – Locomotion – Navigation

41. 10-41 Natural Interfaces Major thrusts in the area of AI and the development of natural interfaces – Natural languages – Speech recognition – Virtual reality Involves research and development in – Linguistics – Psychology – Computer science – Other disciplines

42. 10-42 Latest Commercial Applications of AI Decision Support – Helps capture the why as well as the what of engineered design and decision making Information Retrieval – Distills tidal waves of information into simple presentations – Natural language technology – Database mining

43. 10-43 Latest Commercial Applications of AI Virtual Reality – X-ray-like vision enabled by enhanced-reality visualization helps surgeons – Automated animation and haptic interfaces allow users to interact with virtual objects Robotics – Machine-vision inspections systems – Cutting-edge robotics systems From micro robots and hands and legs, to cognitive and trainable modular vision systems

44. 10-44 Expert Systems An Expert System (ES) – A knowledge-based information system – Contain knowledge about a specific, complex application area – Acts as an expert consultant to end users

45. 10-45 Components of an Expert System Knowledge Base – Facts about a specific subject area – Heuristics that express the reasoning procedures of an expert (rules of thumb) Software Resources – An inference engine processes the knowledge and recommends a course of action – User interface programs communicate with the end user – Explanation programs explain the reasoning process to the end user

46. 10-46 Knowledge Engineering A knowledge engineer –Works with experts to capture the knowledge they possess Facts and rules of thumb –Builds the knowledge base if necessary, the rest of the expert system –Similar role to systems analysts 10-46

47. 10-47 Neural Networks Modeled after the brain’s mesh-like network of interconnected processing elements (neurons) –Interconnected processors operate in parallel and interact with each other –Allows the network to learn from the data it processes

48. 10-48 Genetic Algorithms Genetic algorithm software –Uses Darwinian, randomizing, and other mathematical functions –Simulates an evolutionary process, yielding increasingly better solutions to a problem –Used to model a variety of scientific, technical, and business processes –Useful when thousands of solutions are possible

49. 10-49 Virtual Reality (VR) Virtual reality is a computer-simulated reality –Fast-growing area of artificial intelligence –Originated from efforts to build natural, realistic, multi-sensory human-computer interfaces –Relies on multi-sensory input/output devices –Creates a three-dimensional world through sight, sound, and touch Telepresence –Using VR to perform a task in a different location

50. 10-50 Intelligent Agents Software surrogate for an end user or a process that fulfills a stated need or activity –Uses built-in and learned knowledge base to accomplish tasks Software robots or bots

51. 10-51 Types of Intelligent Agents User Interface Agents –Interface Tutors –Presentation Agents –Network Navigation Agents –Role-Playing Agents Information Management Agents –Search Agents –Information Brokers –Information Filters